The present invention relates to signal and image processing systems such as Automatic Target Recognition (ATR) systems used for target classification and target tracking and Automatic Object Recognition (AOR) systems. The input to these ATR systems could be signals or images of scenes collected from Electro-Optical (EO) sensors, or Radar (RF) sensors or other signal or image generating sensors.
In signal and image processing systems, the signal metrics are the measures of the quality of the signal and consequently they convey information about the scene, the objects in the scene, the sensor, the signal transmission path, and the environmental conditions under which the signal was obtained. These metrics may be divided into the classes of Global Metrics, Local Metrics and Object or Target Metrics. The scene complexity is an example of the Global Metrics. An example of the local metrics is the contrast ratio between objects in the scene and their immediate backgrounds. Target entropy is an example of the object metrics.
It is to be understood that the signal metrics is a more generic term than image metrics and that the principles of the present invention apply to both signal metrics and image metrics even though specific examples will be given in terms of image metrics. In the past the approach used to arrive at the image metrics was a manual approach as illustrated in FIG. 1. In this approach one examines the signal or image for the case of two-dimensional signals and through the use of extensive ground truth information identifies all of the knowable aspects of the scene. The knowable aspects include, for example, the location of the objects in the scene, the type targets or objects and their orientation, the range from the sensor to the objects and to the various regions in the image and other knowable aspects of the scene. The image metrics are then computed in an organized fashion by examining each object separately. An example of a local metric is Target Interference Ratio (TIR) squared. The approach to calculating (TIR) begins by placing a minimum bounding rectangle (MBR) around each object. The metric (TIR).sup.2 is defined as the squared ratio of the difference between the average of target intensity value and the average intensity value of the background as obtained in a rectangular box twice the size of the MBR that surrounds the MBR, and the variance of the intensity values of the background. It should be obvious that to obtain the metrics as just described requires tedious and extensive ground truthing and manual computation. These requirements make the manual computation tedious in the best situations where the extensive ground truth data is available. In the real world situation the above described approach is impractical because the location of the targets and their identities are unknown.
In the past large sets of data have been collected with AOR systems; however it has not been practical to communicate the important features of this data to other users of AOR systems so that useful collected data could be exchanged. This is because the data has not been characterized. In the past this characterization could only be done by the tedious ground truthing process already described.